Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Prog Brain Res. 2013;202:73-98. doi: 10.1016/B978-0-444-62604-2.00005-8.
Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from description or experience), and although prospect theory explains decisions from description, a comprehensive model of decisions from experience is yet to be found. Instance-based learning theory (IBLT) explains how decisions are made from experience through interactions with dynamic environments (Gonzalez et al., 2003). The theory has shown robust explanations of behavior across multiple tasks and contexts, but it is becoming unclear what the theory is able to explain and what it does not. The goal of this chapter is to start addressing this problem. I will introduce IBLT and a recent cognitive model based on this theory: the IBL model of repeated binary choice; then I will discuss the phenomena that the IBL model explains and those that the model does not. The argument is for the theory's robustness but also for clarity in terms of concrete effects that the theory can or cannot account for.
大多数关于人们在风险情境下进行决策的研究都依赖于描述性决策，其中明确说明了结果及其概率。但最近，人们越来越关注基于经验的决策，人们通过探索来发现这些结果和概率。更重要的是，风险行为取决于决策的方式（描述性或经验性），尽管前景理论解释了描述性决策，但还没有找到一个全面的经验性决策模型。基于实例的学习理论（IBLT）通过与动态环境的交互解释了人们如何从经验中做出决策（Gonzalez 等人，2003）。该理论已经对多个任务和情境中的行为进行了强有力的解释，但目前尚不清楚该理论能够解释什么，不能解释什么。本章的目的是开始解决这个问题。我将介绍 IBLT 和一个基于该理论的最新认知模型：重复二项选择的 IBL 模型；然后我将讨论该模型可以解释的现象和不能解释的现象。我的观点是该理论具有稳健性，但也需要在理论可以或不能解释的具体效果方面更加清晰。
